Hi HN, I'm Logan. After watching Replit's agent delete a production database, Claude Code wipe a user's home directory, and Amazon Kiro cause a 13-hour AWS outage, I built the tool I wished existed.
AvaKill intercepts AI agent tool calls — file writes, shell commands, API requests, and evaluates them against a YAML policy file before they execute. No ML, no API calls, no latency. Deterministic policy evaluation in under 1 millisecond.
Three enforcement paths:
- Hooks: Direct integration into Claude Code, Cursor (in testing), Windsurf, Gemini CLI, Codex, Kiro, Amp
- MCP Proxy: Transparent proxy between any MCP client and server (in testing)
- OS Sandbox: Kernel-level enforcement via Landlock (Linux), sandbox-exec (macOS), AppContainer (Windows)
`pipx install avakill` — you're protected in under a minute.
The deterministic angle makes sense. One thing that keeps coming up in real deployments is that teams end up dealing with three separate problems at once: isolation, policy enforcement, and runaway execution. A policy engine can block obviously bad actions, but you still need session budgets / loop caps for the cases where the agent stays "within policy" while doing the wrong thing repeatedly. That boundary is a big part of what pushed us to build Daedalab. Curious how you're drawing it here.
Very cool! Smart way of putting deterministic guardrails on projects instead of trying to stack more ML on top of ML (which is what I always end up trying… to maddening effect). Curious if it can be used/stacked as a primitive to control things like token budgets or spending budgets and other real world activities in OpenClaw
Hi HN, I'm Logan. After watching Replit's agent delete a production database, Claude Code wipe a user's home directory, and Amazon Kiro cause a 13-hour AWS outage, I built the tool I wished existed.
AvaKill intercepts AI agent tool calls — file writes, shell commands, API requests, and evaluates them against a YAML policy file before they execute. No ML, no API calls, no latency. Deterministic policy evaluation in under 1 millisecond.
Three enforcement paths:
- Hooks: Direct integration into Claude Code, Cursor (in testing), Windsurf, Gemini CLI, Codex, Kiro, Amp
- MCP Proxy: Transparent proxy between any MCP client and server (in testing)
- OS Sandbox: Kernel-level enforcement via Landlock (Linux), sandbox-exec (macOS), AppContainer (Windows)
`pipx install avakill` — you're protected in under a minute.
demo video: https://avakill-demo-video.b-cdn.net/avakill_demo.mp4
The deterministic angle makes sense. One thing that keeps coming up in real deployments is that teams end up dealing with three separate problems at once: isolation, policy enforcement, and runaway execution. A policy engine can block obviously bad actions, but you still need session budgets / loop caps for the cases where the agent stays "within policy" while doing the wrong thing repeatedly. That boundary is a big part of what pushed us to build Daedalab. Curious how you're drawing it here.
Very cool! Smart way of putting deterministic guardrails on projects instead of trying to stack more ML on top of ML (which is what I always end up trying… to maddening effect). Curious if it can be used/stacked as a primitive to control things like token budgets or spending budgets and other real world activities in OpenClaw